


"""simple docstring"""



import argparse
import logging
import os
import time
import timeit

import datasets
import numpy as np
import pycuda.autoinit  # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions

import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate


lowercase_							=							trt.Logger(trt.Logger.WARNING)
lowercase_							=							absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

lowercase_							=							logging.getLogger(__name__)

lowercase_							=							argparse.ArgumentParser()

# Required parameters
parser.add_argument(
    '--onnx_model_path',
    default=None,
    type=str,
    required=True,
    help='Path to ONNX model: ',
)

parser.add_argument(
    '--output_dir',
    default=None,
    type=str,
    required=True,
    help='The output directory where the model checkpoints and predictions will be written.',
)

# Other parameters

parser.add_argument(
    '--tokenizer_name',
    default='',
    type=str,
    required=True,
    help='Pretrained tokenizer name or path if not the same as model_name',
)

parser.add_argument(
    '--version_2_with_negative',
    action='store_true',
    help='If true, the SQuAD examples contain some that do not have an answer.',
)
parser.add_argument(
    '--null_score_diff_threshold',
    type=float,
    default=0.0,
    help='If null_score - best_non_null is greater than the threshold predict null.',
)

parser.add_argument(
    '--max_seq_length',
    default=3_84,
    type=int,
    help=(
        'The maximum total input sequence length after WordPiece tokenization. Sequences '
        'longer than this will be truncated, and sequences shorter than this will be padded.'
    ),
)
parser.add_argument(
    '--doc_stride',
    default=1_28,
    type=int,
    help='When splitting up a long document into chunks, how much stride to take between chunks.',
)

parser.add_argument('--per_device_eval_batch_size', default=8, type=int, help='Batch size per GPU/CPU for evaluation.')

parser.add_argument(
    '--n_best_size',
    default=20,
    type=int,
    help='The total number of n-best predictions to generate in the nbest_predictions.json output file.',
)
parser.add_argument(
    '--max_answer_length',
    default=30,
    type=int,
    help=(
        'The maximum length of an answer that can be generated. This is needed because the start '
        'and end predictions are not conditioned on one another.'
    ),
)

parser.add_argument('--seed', type=int, default=42, help='random seed for initialization')

parser.add_argument(
    '--dataset_name',
    type=str,
    default=None,
    required=True,
    help='The name of the dataset to use (via the datasets library).',
)
parser.add_argument(
    '--dataset_config_name',
    type=str,
    default=None,
    help='The configuration name of the dataset to use (via the datasets library).',
)
parser.add_argument(
    '--preprocessing_num_workers', type=int, default=4, help='A csv or a json file containing the training data.'
)
parser.add_argument('--overwrite_cache', action='store_true', help='Overwrite the cached training and evaluation sets')
parser.add_argument(
    '--fp16',
    action='store_true',
    help='Whether to use 16-bit (mixed) precision instead of 32-bit',
)
parser.add_argument(
    '--int8',
    action='store_true',
    help='Whether to use INT8',
)

lowercase_							=							parser.parse_args()

if args.tokenizer_name:
		lowercase_							=							AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
		raise ValueError(
		    'You are instantiating a new tokenizer from scratch. This is not supported by this script.'
		    'You can do it from another script, save it, and load it from here, using --tokenizer_name.'
		)

logger.info('Training/evaluation parameters %s', args)

lowercase_							=							args.per_device_eval_batch_size

lowercase_							=							(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
lowercase_							=							True

lowercase_							=							'temp_engine/bert-fp32.engine'
if args.fpaa:
		lowercase_							=							'temp_engine/bert-fp16.engine'
if args.inta:
		lowercase_							=							'temp_engine/bert-int8.engine'

# import ONNX file
if not os.path.exists('temp_engine'):
		os.makedirs('temp_engine')

lowercase_							=							1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
    network, TRT_LOGGER
) as parser:
		with open(args.onnx_model_path, 'rb') as model:
				if not parser.parse(model.read()):
						for error in range(parser.num_errors):
								print(parser.get_error(error))

    # Query input names and shapes from parsed TensorRT network
		lowercase_							=							[network.get_input(i) for i in range(network.num_inputs)]
		lowercase_							=							[_input.name for _input in network_inputs]  # ex: ["actual_input1"]

		with builder.create_builder_config() as config:
				lowercase_							=							1 << 50
				if STRICT_TYPES:
						config.set_flag(trt.BuilderFlag.STRICT_TYPES)
				if args.fpaa:
						config.set_flag(trt.BuilderFlag.FPaa)
				if args.inta:
						config.set_flag(trt.BuilderFlag.INTa)
				lowercase_							=							builder.create_optimization_profile()
				config.add_optimization_profile(profile)
				for i in range(len(input_names)):
						profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
				lowercase_							=							builder.build_engine(network, config)

				# serialize_engine and store in file (can be directly loaded and deserialized):
				with open(engine_name, 'wb') as f:
						f.write(engine.serialize())
def      UpperCAmelCase     (       _lowercase :   Union[str, Any]			,   _lowercase :   Optional[int]			,   _lowercase :   int			,   _lowercase :   List[Any]			,   _lowercase :   Dict			,   _lowercase :   List[str]			,   _lowercase :   Any			,   _lowercase :   Tuple	)						-> Optional[Any]:


	"""simple docstring"""




	lowerCAmelCase_      							=			np.asarray(inputs['''input_ids''']			,   dtype=np.intaa	)
	lowerCAmelCase_      							=			np.asarray(inputs['''attention_mask''']			,   dtype=np.intaa	)
	lowerCAmelCase_      							=			np.asarray(inputs['''token_type_ids''']			,   dtype=np.intaa	)

	# Copy inputs
	cuda.memcpy_htod_async(d_inputs[0]			,   input_ids.ravel()			,   _lowercase	)
	cuda.memcpy_htod_async(d_inputs[1]			,   attention_mask.ravel()			,   _lowercase	)
	cuda.memcpy_htod_async(d_inputs[2]			,   token_type_ids.ravel()			,   _lowercase	)
	# start time
	lowerCAmelCase_      							=			time.time()
	# Run inference
	context.execute_async(
	    bindings=[int(_lowercase	) for d_inp in d_inputs] + [int(_lowercase	), int(_lowercase	)]			,   stream_handle=stream.handle	)
	# Transfer predictions back from GPU
	cuda.memcpy_dtoh_async(_lowercase			,   _lowercase			,   _lowercase	)
	cuda.memcpy_dtoh_async(_lowercase			,   _lowercase			,   _lowercase	)
	# Synchronize the stream and take time
	stream.synchronize()
	# end time
	lowerCAmelCase_      							=			time.time()
	lowerCAmelCase_      							=			end_time - start_time
	lowerCAmelCase_      							=			(h_outputa, h_outputa)
	# print(outputs)
	return outputs, infer_time


# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
lowercase_							=							Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)

# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
		datasets.utils.logging.set_verbosity_warning()
		transformers.utils.logging.set_verbosity_info()
else:
		datasets.utils.logging.set_verbosity_error()
		transformers.utils.logging.set_verbosity_error()

# If passed along, set the training seed now.
if args.seed is not None:
		set_seed(args.seed)

# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
		# Downloading and loading a dataset from the hub.
		lowercase_							=							load_dataset(args.dataset_name, args.dataset_config_name)
else:
		raise ValueError('Evaluation requires a dataset name')
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.

lowercase_							=							raw_datasets['validation'].column_names

lowercase_							=							'question' if 'question' in column_names else column_names[0]
lowercase_							=							'context' if 'context' in column_names else column_names[1]
lowercase_							=							'answers' if 'answers' in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
lowercase_							=							tokenizer.padding_side == 'right'

if args.max_seq_length > tokenizer.model_max_length:
		logger.warning(
		    f"""The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the"""
		    f"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."""
		)

lowercase_							=							min(args.max_seq_length, tokenizer.model_max_length)
def      UpperCAmelCase     (       _lowercase :   str	)						-> int:


	"""simple docstring"""




	lowerCAmelCase_      							=			[q.lstrip() for q in examples[question_column_name]]

	# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
	# in one example possible giving several features when a context is long, each of those features having a
	# context that overlaps a bit the context of the previous feature.
	lowerCAmelCase_      							=			tokenizer(
	    examples[question_column_name if pad_on_right else context_column_name]			,   examples[context_column_name if pad_on_right else question_column_name]			,   truncation='''only_second''' if pad_on_right else '''only_first'''			,   max_length=_lowercase			,   stride=args.doc_stride			,   return_overflowing_tokens=_lowercase			,   return_offsets_mapping=_lowercase			,   padding='''max_length'''			,   )

	# Since one example might give us several features if it has a long context, we need a map from a feature to
	# its corresponding example. This key gives us just that.
	lowerCAmelCase_      							=			tokenized_examples.pop('''overflow_to_sample_mapping'''	)

	# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
	# corresponding example_id and we will store the offset mappings.
	lowerCAmelCase_      							=			[]

	for i in range(len(tokenized_examples['''input_ids''']	)	):
		# Grab the sequence corresponding to that example (to know what is the context and what is the question).
		lowerCAmelCase_      							=			tokenized_examples.sequence_ids(_lowercase	)
		lowerCAmelCase_      							=			1 if pad_on_right else 0

		# One example can give several spans, this is the index of the example containing this span of text.
		lowerCAmelCase_      							=			sample_mapping[i]
		tokenized_examples["example_id"].append(examples['''id'''][sample_index]	)

		# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
		# position is part of the context or not.
		lowerCAmelCase_      							=			[
		    (o if sequence_ids[k] == context_index else None)
		    for k, o in enumerate(tokenized_examples['''offset_mapping'''][i]	)
		]

	return tokenized_examples


lowercase_							=							raw_datasets['validation']
# Validation Feature Creation
lowercase_							=							eval_examples.map(
    prepare_validation_features,
    batched=True,
    num_proc=args.preprocessing_num_workers,
    remove_columns=column_names,
    load_from_cache_file=not args.overwrite_cache,
    desc='Running tokenizer on validation dataset',
)

lowercase_							=							default_data_collator

lowercase_							=							eval_dataset.remove_columns(['example_id', 'offset_mapping'])
lowercase_							=							DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)
def      UpperCAmelCase     (       _lowercase :   int			,   _lowercase :   Tuple			,   _lowercase :   List[str]			,   _lowercase :   Any="eval"	)						-> Dict:


	"""simple docstring"""




	lowerCAmelCase_      							=			postprocess_qa_predictions(
	    examples=_lowercase			,   features=_lowercase			,   predictions=_lowercase			,   version_2_with_negative=args.version_2_with_negative			,   n_best_size=args.n_best_size			,   max_answer_length=args.max_answer_length			,   null_score_diff_threshold=args.null_score_diff_threshold			,   output_dir=args.output_dir			,   prefix=_lowercase			,   )
	# Format the result to the format the metric expects.
	if args.version_2_with_negative:
		lowerCAmelCase_      							=			[
		    {'''id''': k, '''prediction_text''': v, '''no_answer_probability''': 0.0} for k, v in predictions.items()
		]
	else:
		lowerCAmelCase_      							=			[{'''id''': k, '''prediction_text''': v} for k, v in predictions.items()]

	lowerCAmelCase_      							=			[{'''id''': ex['''id'''], '''answers''': ex[answer_column_name]} for ex in examples]
	return EvalPrediction(predictions=_lowercase			,   label_ids=_lowercase	)


lowercase_							=							load_metric('squad_v2' if args.version_2_with_negative else 'squad')

# Evaluation!
logger.info('Loading ONNX model %s for evaluation', args.onnx_model_path)
with open(engine_name, 'rb') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
    f.read()
) as engine, engine.create_execution_context() as context:
		# setup for TRT inferrence
		for i in range(len(input_names)):
				context.set_binding_shape(i, INPUT_SHAPE)
		assert context.all_binding_shapes_specified
		def      UpperCAmelCase     (       _lowercase :   List[str]	)						-> List[str]:


			"""simple docstring"""




			return trt.volume(engine.get_binding_shape(_lowercase	)	) * engine.get_binding_dtype(_lowercase	).itemsize

		# Allocate device memory for inputs and outputs.
		lowercase_							=							[cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]

		# Allocate output buffer
		lowercase_							=							cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
		lowercase_							=							cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
		lowercase_							=							cuda.mem_alloc(h_outputa.nbytes)
		lowercase_							=							cuda.mem_alloc(h_outputa.nbytes)

		# Create a stream in which to copy inputs/outputs and run inference.
		lowercase_							=							cuda.Stream()

		# Evaluation
		logger.info('***** Running Evaluation *****')
		logger.info(f"""  Num examples = {len(eval_dataset)}""")
		logger.info(f"""  Batch size = {args.per_device_eval_batch_size}""")

		lowercase_							=							0.0
		lowercase_							=							0
		lowercase_							=							timeit.default_timer()

		lowercase_							=							None
		for step, batch in enumerate(eval_dataloader):
				lowercase_			,	lowercase_							=							model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
				total_time += infer_time
				niter += 1

				lowercase_			,	lowercase_							=							outputs
				lowercase_							=							torch.tensor(start_logits)
				lowercase_							=							torch.tensor(end_logits)

				# necessary to pad predictions and labels for being gathered
				lowercase_							=							accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_00)
				lowercase_							=							accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_00)

				lowercase_							=							(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
				lowercase_							=							logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_00)

		if all_preds is not None:
				lowercase_							=							nested_truncate(all_preds, len(eval_dataset))

		lowercase_							=							timeit.default_timer() - start_time
		logger.info('  Evaluation done in total %f secs (%f sec per example)', evalTime, evalTime / len(eval_dataset))
		# Inference time from TRT
		logger.info('Average Inference Time = {:.3f} ms'.format(total_time * 10_00 / niter))
		logger.info('Total Inference Time =  {:.3f} ms'.format(total_time * 10_00))





		logger.info('Total Number of Inference =  %d', niter)

lowercase_							=							post_processing_function(eval_examples, eval_dataset, all_preds)
lowercase_							=							metric.compute(predictions=prediction.predictions, references=prediction.label_ids)





logger.info(f"""Evaluation metrics: {eval_metric}""")



"""simple docstring"""



import math
from datetime import datetime, timedelta
def      UpperCAmelCase     (       _lowercase :   int	)						-> datetime:


	"""simple docstring"""




	lowerCAmelCase_      							=			year % 1_9
	lowerCAmelCase_      							=			year % 4
	lowerCAmelCase_      							=			year % 7
	lowerCAmelCase_      							=			math.floor(year / 1_0_0	)
	lowerCAmelCase_      							=			math.floor((1_3 + 8 * leap_day_inhibits) / 2_5	)
	lowerCAmelCase_      							=			leap_day_inhibits / 4
	lowerCAmelCase_      							=			(
	    1_5 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
	) % 3_0
	lowerCAmelCase_      							=			(4 + leap_day_inhibits - leap_day_reinstall_number) % 7

	# days to be added to March 21
	lowerCAmelCase_      							=			(1_9 * metonic_cycle + secular_moon_shift) % 3_0

	# PHM -> Paschal Full Moon
	lowerCAmelCase_      							=			(
	    2 * julian_leap_year
	    + 4 * non_leap_year
	    + 6 * days_to_add
	    + century_starting_point
	) % 7

	if days_to_add == 2_9 and days_from_phm_to_sunday == 6:
		return datetime(_lowercase			,   4			,   1_9	)
	elif days_to_add == 2_8 and days_from_phm_to_sunday == 6:
		return datetime(_lowercase			,   4			,   1_8	)
	else:
		return datetime(_lowercase			,   3			,   2_2	) + timedelta(
		    days=int(days_to_add + days_from_phm_to_sunday	)	)


if __name__ == "__main__":
		for year in (19_94, 20_00, 20_10, 20_21, 20_23):
				lowercase_							=							'will be' if year > datetime.now().year else 'was'
				print(f"""Easter in {year} {tense} {gauss_easter(year)}""")